{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b4314f8",
   "metadata": {},
   "source": [
    "Testing association between age at vaccination and vaccine response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33bb7c1f-ea24-4a2e-b8de-f270054af3a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rcParams['figure.dpi'] = 150\n",
    "\n",
    "from scipy import stats\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da2fd85e-3f40-48a5-a2d9-c091cd0129dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '../data/'\n",
    "metadata_path = '../data/metadata/'\n",
    "plot_path = '../figures/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c7ae57",
   "metadata": {},
   "source": [
    "### Data set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91148928-5d4e-4181-9ca2-6242148cbb59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject ID</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Birth Height (cm)</th>\n",
       "      <th>Birth Height Percentile</th>\n",
       "      <th>Birth Weight (kg)</th>\n",
       "      <th>Birth Weight Percentile</th>\n",
       "      <th>Birth Head Cir. (cm)</th>\n",
       "      <th>Birth Head Cir. Percentile</th>\n",
       "      <th>Race #1</th>\n",
       "      <th>...</th>\n",
       "      <th>Amphibian/Reptile</th>\n",
       "      <th>Bird</th>\n",
       "      <th>Other</th>\n",
       "      <th>Live on Farm?</th>\n",
       "      <th>Smokers at home?</th>\n",
       "      <th>Any medical conditions/signs/symptoms prior to study?</th>\n",
       "      <th>Condition #1</th>\n",
       "      <th>Past or Current?</th>\n",
       "      <th>Condition #2</th>\n",
       "      <th>Past or Current?.1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PrimaryKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baby101</th>\n",
       "      <td>101</td>\n",
       "      <td>2018-01-24</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>3.646</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>Not Documented</td>\n",
       "      <td>White/Caucasian</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Nevus</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby102</th>\n",
       "      <td>102</td>\n",
       "      <td>2018-02-20</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>3.35</td>\n",
       "      <td>37</td>\n",
       "      <td>34.5</td>\n",
       "      <td>25</td>\n",
       "      <td>Arab/North African</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby103</th>\n",
       "      <td>103</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>Female</td>\n",
       "      <td>52</td>\n",
       "      <td>84</td>\n",
       "      <td>3.41</td>\n",
       "      <td>51</td>\n",
       "      <td>36.5</td>\n",
       "      <td>85</td>\n",
       "      <td>White/Caucasian</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>broken collar bone</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby104</th>\n",
       "      <td>104</td>\n",
       "      <td>2018-03-12</td>\n",
       "      <td>Female</td>\n",
       "      <td>55.2</td>\n",
       "      <td>98</td>\n",
       "      <td>3.615</td>\n",
       "      <td>61</td>\n",
       "      <td>35.6</td>\n",
       "      <td>64</td>\n",
       "      <td>White/Caucasian</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby105</th>\n",
       "      <td>105</td>\n",
       "      <td>2018-03-30</td>\n",
       "      <td>Female</td>\n",
       "      <td>50.8</td>\n",
       "      <td>72.31</td>\n",
       "      <td>3.97</td>\n",
       "      <td>89.09</td>\n",
       "      <td>34</td>\n",
       "      <td>32.73</td>\n",
       "      <td>White/Caucasian</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pigs</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Jaundice</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Subject ID Date of Birth  Gender Birth Height (cm)  \\\n",
       "PrimaryKey                                                       \n",
       "Baby101            101    2018-01-24    Male    Not Documented   \n",
       "Baby102            102    2018-02-20    Male                50   \n",
       "Baby103            103    2018-02-21  Female                52   \n",
       "Baby104            104    2018-03-12  Female              55.2   \n",
       "Baby105            105    2018-03-30  Female              50.8   \n",
       "\n",
       "           Birth Height Percentile Birth Weight (kg) Birth Weight Percentile  \\\n",
       "PrimaryKey                                                                     \n",
       "Baby101             Not Documented             3.646          Not Documented   \n",
       "Baby102                         50              3.35                      37   \n",
       "Baby103                         84              3.41                      51   \n",
       "Baby104                         98             3.615                      61   \n",
       "Baby105                      72.31              3.97                   89.09   \n",
       "\n",
       "           Birth Head Cir. (cm) Birth Head Cir. Percentile  \\\n",
       "PrimaryKey                                                   \n",
       "Baby101          Not Documented             Not Documented   \n",
       "Baby102                    34.5                         25   \n",
       "Baby103                    36.5                         85   \n",
       "Baby104                    35.6                         64   \n",
       "Baby105                      34                      32.73   \n",
       "\n",
       "                       Race #1  ... Amphibian/Reptile Bird  Other  \\\n",
       "PrimaryKey                      ...                                 \n",
       "Baby101        White/Caucasian  ...               NaN  NaN    NaN   \n",
       "Baby102     Arab/North African  ...               NaN  NaN    NaN   \n",
       "Baby103        White/Caucasian  ...               NaN  NaN    NaN   \n",
       "Baby104        White/Caucasian  ...               NaN  NaN    NaN   \n",
       "Baby105        White/Caucasian  ...               NaN  NaN   pigs   \n",
       "\n",
       "           Live on Farm? Smokers at home?  \\\n",
       "PrimaryKey                                  \n",
       "Baby101               No               No   \n",
       "Baby102               No               No   \n",
       "Baby103               No               No   \n",
       "Baby104               No               No   \n",
       "Baby105               No               No   \n",
       "\n",
       "           Any medical conditions/signs/symptoms prior to study?  \\\n",
       "PrimaryKey                                                         \n",
       "Baby101                                                   Yes      \n",
       "Baby102                                                    No      \n",
       "Baby103                                                   Yes      \n",
       "Baby104                                                    No      \n",
       "Baby105                                                   Yes      \n",
       "\n",
       "                  Condition #1 Past or Current? Condition #2  \\\n",
       "PrimaryKey                                                     \n",
       "Baby101                  Nevus          Current          NaN   \n",
       "Baby102                    NaN              NaN          NaN   \n",
       "Baby103     broken collar bone          Current          NaN   \n",
       "Baby104                    NaN              NaN          NaN   \n",
       "Baby105               Jaundice          Current          NaN   \n",
       "\n",
       "           Past or Current?.1  \n",
       "PrimaryKey                     \n",
       "Baby101                   NaN  \n",
       "Baby102                   NaN  \n",
       "Baby103                   NaN  \n",
       "Baby104                   NaN  \n",
       "Baby105                   NaN  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# participants information\n",
    "participants = pd.read_csv(os.path.join(metadata_path, 'participants.tsv'), sep='\\t', index_col=0)\n",
    "participants['Date of Birth'] = pd.to_datetime(participants['Date of Birth'])\n",
    "participants.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "555f29a0-5c5c-4b39-84b3-7b00fa8e3601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>Date</th>\n",
       "      <th>VisitCode</th>\n",
       "      <th>VisitType</th>\n",
       "      <th>Age(Days)</th>\n",
       "      <th>Category</th>\n",
       "      <th>CollectionMethod</th>\n",
       "      <th>SampleType</th>\n",
       "      <th>Antigen</th>\n",
       "      <th>Value</th>\n",
       "      <th>Unit</th>\n",
       "      <th>ProtectiveThreshold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PrimaryKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baby106_2m_PT</th>\n",
       "      <td>Baby106</td>\n",
       "      <td>2018-05-29</td>\n",
       "      <td>V5</td>\n",
       "      <td>Well Check 02m</td>\n",
       "      <td>63</td>\n",
       "      <td>2m</td>\n",
       "      <td>Heelstick</td>\n",
       "      <td>Serum</td>\n",
       "      <td>PT</td>\n",
       "      <td>5.00</td>\n",
       "      <td>ELU/ml</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby110_2m_PT</th>\n",
       "      <td>Baby110</td>\n",
       "      <td>2018-07-09</td>\n",
       "      <td>V5</td>\n",
       "      <td>Well Check 02m</td>\n",
       "      <td>63</td>\n",
       "      <td>2m</td>\n",
       "      <td>Heelstick</td>\n",
       "      <td>Serum</td>\n",
       "      <td>PT</td>\n",
       "      <td>60.00</td>\n",
       "      <td>ELU/ml</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby113_2m_PT</th>\n",
       "      <td>Baby113</td>\n",
       "      <td>2018-07-26</td>\n",
       "      <td>V5</td>\n",
       "      <td>Well Check 02m</td>\n",
       "      <td>72</td>\n",
       "      <td>2m</td>\n",
       "      <td>Heelstick</td>\n",
       "      <td>Serum</td>\n",
       "      <td>PT</td>\n",
       "      <td>22.00</td>\n",
       "      <td>ELU/ml</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby113_2m_Dip</th>\n",
       "      <td>Baby113</td>\n",
       "      <td>2018-07-26</td>\n",
       "      <td>V5</td>\n",
       "      <td>Well Check 02m</td>\n",
       "      <td>72</td>\n",
       "      <td>2m</td>\n",
       "      <td>Heelstick</td>\n",
       "      <td>Serum</td>\n",
       "      <td>Dip</td>\n",
       "      <td>0.58</td>\n",
       "      <td>IU/ml</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baby115_2m_PT</th>\n",
       "      <td>Baby115</td>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>V6</td>\n",
       "      <td>Well Check 04m</td>\n",
       "      <td>92</td>\n",
       "      <td>2m</td>\n",
       "      <td>Heelstick</td>\n",
       "      <td>Serum</td>\n",
       "      <td>PT</td>\n",
       "      <td>5.00</td>\n",
       "      <td>ELU/ml</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  BabyN       Date VisitCode       VisitType  Age(Days)  \\\n",
       "PrimaryKey                                                                \n",
       "Baby106_2m_PT   Baby106 2018-05-29        V5  Well Check 02m         63   \n",
       "Baby110_2m_PT   Baby110 2018-07-09        V5  Well Check 02m         63   \n",
       "Baby113_2m_PT   Baby113 2018-07-26        V5  Well Check 02m         72   \n",
       "Baby113_2m_Dip  Baby113 2018-07-26        V5  Well Check 02m         72   \n",
       "Baby115_2m_PT   Baby115 2018-08-24        V6  Well Check 04m         92   \n",
       "\n",
       "               Category CollectionMethod SampleType Antigen  Value    Unit  \\\n",
       "PrimaryKey                                                                   \n",
       "Baby106_2m_PT        2m        Heelstick      Serum      PT   5.00  ELU/ml   \n",
       "Baby110_2m_PT        2m        Heelstick      Serum      PT  60.00  ELU/ml   \n",
       "Baby113_2m_PT        2m        Heelstick      Serum      PT  22.00  ELU/ml   \n",
       "Baby113_2m_Dip       2m        Heelstick      Serum     Dip   0.58   IU/ml   \n",
       "Baby115_2m_PT        2m        Heelstick      Serum      PT   5.00  ELU/ml   \n",
       "\n",
       "                ProtectiveThreshold  \n",
       "PrimaryKey                           \n",
       "Baby106_2m_PT                   8.0  \n",
       "Baby110_2m_PT                   8.0  \n",
       "Baby113_2m_PT                   8.0  \n",
       "Baby113_2m_Dip                  0.1  \n",
       "Baby115_2m_PT                   8.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# titres information -- use this to get date that 1y response is measured\n",
    "titres = pd.read_csv(os.path.join(data_path, 'vaccine_response', 'titers.tsv'), sep='\\t', index_col=0)\n",
    "titres['Date'] = pd.to_datetime(titres['Date'])\n",
    "titres.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df236bab-3fe5-4f8f-ab92-9d5648b5643b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>PT</th>\n",
       "      <th>Dip</th>\n",
       "      <th>FHA</th>\n",
       "      <th>PRN</th>\n",
       "      <th>TET</th>\n",
       "      <th>PRP (Hib)</th>\n",
       "      <th>PCV ST1</th>\n",
       "      <th>PCV ST3</th>\n",
       "      <th>PCV ST4</th>\n",
       "      <th>...</th>\n",
       "      <th>median_mmNorm</th>\n",
       "      <th>median_mmNorm_DTAPHib</th>\n",
       "      <th>median_mmNorm_PCV</th>\n",
       "      <th>PT_protected</th>\n",
       "      <th>Dip_protected</th>\n",
       "      <th>FHA_protected</th>\n",
       "      <th>PRN_protected</th>\n",
       "      <th>TET_protected</th>\n",
       "      <th>PRP (Hib)_protected</th>\n",
       "      <th>VR_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baby106</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.21</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.39</td>\n",
       "      <td>141.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061955</td>\n",
       "      <td>0.052874</td>\n",
       "      <td>0.061955</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baby107</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.44</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.449483</td>\n",
       "      <td>0.114018</td>\n",
       "      <td>0.958142</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baby108</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.27</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>LVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Baby109</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>7.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.700925</td>\n",
       "      <td>0.763049</td>\n",
       "      <td>0.486810</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baby110</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.266219</td>\n",
       "      <td>0.284211</td>\n",
       "      <td>0.245121</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     BabyN    PT   Dip   FHA   PRN   TET  PRP (Hib)  PCV ST1  PCV ST3  \\\n",
       "0  Baby106   2.5  0.21  11.0   2.5  0.30       0.39    141.0     35.0   \n",
       "1  Baby107   2.5  0.44   3.0   9.0  0.52       1.60   2430.0    415.0   \n",
       "2  Baby108   2.5  0.05   1.5   2.5  0.05       0.27     21.0      3.0   \n",
       "3  Baby109  27.0   NaN   NaN  63.0  1.35       7.02      NaN      NaN   \n",
       "4  Baby110  14.0  0.24  15.0  20.0  2.45        NaN    301.0     63.0   \n",
       "\n",
       "   PCV ST4  ...  median_mmNorm  median_mmNorm_DTAPHib  median_mmNorm_PCV  \\\n",
       "0     56.0  ...       0.061955               0.052874           0.061955   \n",
       "1    194.0  ...       0.449483               0.114018           0.958142   \n",
       "2     24.0  ...       0.000000               0.000000           0.003102   \n",
       "3      NaN  ...       0.700925               0.763049           0.486810   \n",
       "4    400.0  ...       0.266219               0.284211           0.245121   \n",
       "\n",
       "   PT_protected  Dip_protected  FHA_protected  PRN_protected  TET_protected  \\\n",
       "0         False           True           True          False           True   \n",
       "1         False           True          False           True           True   \n",
       "2         False          False          False          False          False   \n",
       "3          True          False          False           True           True   \n",
       "4          True           True           True           True           True   \n",
       "\n",
       "   PRP (Hib)_protected  VR_group  \n",
       "0                 True       NVR  \n",
       "1                 True       NVR  \n",
       "2                 True       LVR  \n",
       "3                 True       NVR  \n",
       "4                False       NVR  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(72, 49)\n"
     ]
    }
   ],
   "source": [
    "# vaccine response information\n",
    "y1_vax_response = pd.read_csv(os.path.join(data_path, 'vaccine_response', 'vaccine_response_y1.tsv'), sep='\\t')\n",
    "display(y1_vax_response.head())\n",
    "print(y1_vax_response.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ebbd5a2-7a6a-4d61-a28b-28b6dbf572e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PrimaryKey</th>\n",
       "      <th>BabyN</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Visit Date</th>\n",
       "      <th>Age (Days)</th>\n",
       "      <th>Vaccine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101_DTaP_1</td>\n",
       "      <td>101</td>\n",
       "      <td>1/24/18</td>\n",
       "      <td>3/26/18</td>\n",
       "      <td>61</td>\n",
       "      <td>DTaP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102_DTaP_1</td>\n",
       "      <td>102</td>\n",
       "      <td>2/20/18</td>\n",
       "      <td>4/20/18</td>\n",
       "      <td>59</td>\n",
       "      <td>DTaP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102_DTaP_2</td>\n",
       "      <td>102</td>\n",
       "      <td>2/20/18</td>\n",
       "      <td>6/20/18</td>\n",
       "      <td>120</td>\n",
       "      <td>DTaP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103_DTaP_1</td>\n",
       "      <td>103</td>\n",
       "      <td>2/21/18</td>\n",
       "      <td>4/27/18</td>\n",
       "      <td>65</td>\n",
       "      <td>DTaP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103_DTaP_2</td>\n",
       "      <td>103</td>\n",
       "      <td>2/21/18</td>\n",
       "      <td>8/24/18</td>\n",
       "      <td>184</td>\n",
       "      <td>DTaP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PrimaryKey  BabyN Date of Birth Visit Date  Age (Days) Vaccine\n",
       "0  101_DTaP_1    101       1/24/18    3/26/18          61    DTaP\n",
       "1  102_DTaP_1    102       2/20/18    4/20/18          59    DTaP\n",
       "2  102_DTaP_2    102       2/20/18    6/20/18         120    DTaP\n",
       "3  103_DTaP_1    103       2/21/18    4/27/18          65    DTaP\n",
       "4  103_DTaP_2    103       2/21/18    8/24/18         184    DTaP"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vaccine dose information\n",
    "vax_dates = pd.read_csv(os.path.join(metadata_path, 'vaccines.tsv'), sep='\\t')\n",
    "vax_dates.head()\n",
    "\n",
    "# # turns out the additional information we got via email from eduardo isn't in vaccines.tsv -- this is my mistake\n",
    "# vax_dates = pd.read_csv('../../data/metadata/original_data_sheets/vaxdates_annotated.csv')\n",
    "# display(vax_dates.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77028a11-a023-4514-acdc-20166843addb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # there are some issues with this table: try fixing them by hand\n",
    "\n",
    "# # infant 215 is in this table twice, with 2 DOBs but same vax dates -- remove the incorrect one\n",
    "# # display(vax_dates[vax_dates.BabyN==215])\n",
    "# # display(participants.loc['Baby215'])\n",
    "# dob_215 = pd.to_datetime(participants.loc['Baby215']['Date of Birth']).date()\n",
    "# # print(dob_215)\n",
    "# # print(vax_dates.shape)\n",
    "# vax_dates = vax_dates[~((vax_dates.BabyN==215)&(vax_dates['Date of Birth']!='03/29/2018'))] # what a fucking bodge\n",
    "# # display(vax_dates[vax_dates.BabyN==215])\n",
    "\n",
    "# # infant 118 doesn't have any vaccinations - should be removed from (all?) analysis\n",
    "# vax_dates = vax_dates[~(vax_dates.BabyN==118)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4cf6da7-6baf-4e78-911f-a60d8ea9af5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pt/1n34csq15fs5kqwjkx79wsym0000gn/T/ipykernel_6401/779033365.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  vax_resp['BabyN'] = vax_resp.BabyN.str.lstrip('Baby').astype(int)\n"
     ]
    }
   ],
   "source": [
    "# Arrange data for plotting\n",
    "\n",
    "# filter on kids with y1 vaccine response information\n",
    "vax_dates = vax_dates.loc[('Baby'+vax_dates.BabyN.astype(str)).isin(y1_vax_response.BabyN)]\n",
    "# add dose and ag_dose\n",
    "vax_dates['dose'] = [row.PrimaryKey.split('_')[2] for irow, row in vax_dates.iterrows()]\n",
    "vax_dates['ag_dose'] = [row.Vaccine+'_'+row.PrimaryKey.split('_')[2] for irow, row in vax_dates.iterrows()]\n",
    "# restrict to first three doses\n",
    "vax_dates = vax_dates[vax_dates.dose.astype(int) <=3]\n",
    "# make long\n",
    "vax_dates_wide = vax_dates.pivot(index='BabyN', columns='ag_dose', values='Age (Days)')\n",
    "# take min for dose 1, 2, 3\n",
    "vax_dates_wide['Dose1'] = vax_dates_wide[['DTaP_1','HiB_1','Prevnar 13_1']].min(axis=1)\n",
    "vax_dates_wide['Dose2'] = vax_dates_wide[['DTaP_2','HiB_2','Prevnar 13_2']].min(axis=1)\n",
    "vax_dates_wide['Dose3'] = vax_dates_wide[['DTaP_3','HiB_3','Prevnar 13_3']].min(axis=1)\n",
    "# add when titers were measured\n",
    "titer_age = pd.DataFrame(titres[titres.Category=='1y'][['BabyN','Age(Days)']].groupby('BabyN')['Age(Days)'].agg('median')).reset_index()\n",
    "titer_age.rename(columns={'Age(Days)':'TiterY1'}, inplace=True)\n",
    "titer_age['BabyN'] = titer_age.BabyN.str.lstrip('Baby').astype(int)\n",
    "vax_dates_wide = vax_dates_wide.merge(titer_age, on='BabyN')\n",
    "# add vaccine response information\n",
    "vax_resp = y1_vax_response[['BabyN','VR_group','median_mmNorm','median_mmNorm_DTAPHib','median_mmNorm_PCV']]\n",
    "vax_resp['BabyN'] = vax_resp.BabyN.str.lstrip('Baby').astype(int)\n",
    "vax_dates_wide = vax_dates_wide.merge(vax_resp, on='BabyN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb456937-babd-4419-9b1b-089b3c12ebe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>DTaP_1</th>\n",
       "      <th>DTaP_2</th>\n",
       "      <th>DTaP_3</th>\n",
       "      <th>HiB_1</th>\n",
       "      <th>HiB_2</th>\n",
       "      <th>HiB_3</th>\n",
       "      <th>Prevnar 13_1</th>\n",
       "      <th>Prevnar 13_2</th>\n",
       "      <th>Prevnar 13_3</th>\n",
       "      <th>Dose1</th>\n",
       "      <th>Dose2</th>\n",
       "      <th>Dose3</th>\n",
       "      <th>TiterY1</th>\n",
       "      <th>VR_group</th>\n",
       "      <th>median_mmNorm</th>\n",
       "      <th>median_mmNorm_DTAPHib</th>\n",
       "      <th>median_mmNorm_PCV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106</td>\n",
       "      <td>63</td>\n",
       "      <td>126</td>\n",
       "      <td>217</td>\n",
       "      <td>63</td>\n",
       "      <td>126</td>\n",
       "      <td>217</td>\n",
       "      <td>63</td>\n",
       "      <td>126</td>\n",
       "      <td>217</td>\n",
       "      <td>63</td>\n",
       "      <td>126</td>\n",
       "      <td>217</td>\n",
       "      <td>371.0</td>\n",
       "      <td>NVR</td>\n",
       "      <td>0.061955</td>\n",
       "      <td>0.052874</td>\n",
       "      <td>0.061955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107</td>\n",
       "      <td>63</td>\n",
       "      <td>137</td>\n",
       "      <td>200</td>\n",
       "      <td>63</td>\n",
       "      <td>137</td>\n",
       "      <td>200</td>\n",
       "      <td>63</td>\n",
       "      <td>137</td>\n",
       "      <td>200</td>\n",
       "      <td>63</td>\n",
       "      <td>137</td>\n",
       "      <td>200</td>\n",
       "      <td>378.0</td>\n",
       "      <td>NVR</td>\n",
       "      <td>0.449483</td>\n",
       "      <td>0.114018</td>\n",
       "      <td>0.958142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108</td>\n",
       "      <td>62</td>\n",
       "      <td>127</td>\n",
       "      <td>190</td>\n",
       "      <td>62</td>\n",
       "      <td>127</td>\n",
       "      <td>190</td>\n",
       "      <td>62</td>\n",
       "      <td>127</td>\n",
       "      <td>190</td>\n",
       "      <td>62</td>\n",
       "      <td>127</td>\n",
       "      <td>190</td>\n",
       "      <td>370.0</td>\n",
       "      <td>LVR</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>109</td>\n",
       "      <td>62</td>\n",
       "      <td>126</td>\n",
       "      <td>191</td>\n",
       "      <td>62</td>\n",
       "      <td>126</td>\n",
       "      <td>191</td>\n",
       "      <td>62</td>\n",
       "      <td>126</td>\n",
       "      <td>191</td>\n",
       "      <td>62</td>\n",
       "      <td>126</td>\n",
       "      <td>191</td>\n",
       "      <td>440.0</td>\n",
       "      <td>NVR</td>\n",
       "      <td>0.700925</td>\n",
       "      <td>0.763049</td>\n",
       "      <td>0.486810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110</td>\n",
       "      <td>63</td>\n",
       "      <td>126</td>\n",
       "      <td>189</td>\n",
       "      <td>63</td>\n",
       "      <td>126</td>\n",
       "      <td>189</td>\n",
       "      <td>63</td>\n",
       "      <td>126</td>\n",
       "      <td>189</td>\n",
       "      <td>63</td>\n",
       "      <td>126</td>\n",
       "      <td>189</td>\n",
       "      <td>375.0</td>\n",
       "      <td>NVR</td>\n",
       "      <td>0.266219</td>\n",
       "      <td>0.284211</td>\n",
       "      <td>0.245121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BabyN  DTaP_1  DTaP_2  DTaP_3  HiB_1  HiB_2  HiB_3  Prevnar 13_1  \\\n",
       "0    106      63     126     217     63    126    217            63   \n",
       "1    107      63     137     200     63    137    200            63   \n",
       "2    108      62     127     190     62    127    190            62   \n",
       "3    109      62     126     191     62    126    191            62   \n",
       "4    110      63     126     189     63    126    189            63   \n",
       "\n",
       "   Prevnar 13_2  Prevnar 13_3  Dose1  Dose2  Dose3  TiterY1 VR_group  \\\n",
       "0           126           217     63    126    217    371.0      NVR   \n",
       "1           137           200     63    137    200    378.0      NVR   \n",
       "2           127           190     62    127    190    370.0      LVR   \n",
       "3           126           191     62    126    191    440.0      NVR   \n",
       "4           126           189     63    126    189    375.0      NVR   \n",
       "\n",
       "   median_mmNorm  median_mmNorm_DTAPHib  median_mmNorm_PCV  \n",
       "0       0.061955               0.052874           0.061955  \n",
       "1       0.449483               0.114018           0.958142  \n",
       "2       0.000000               0.000000           0.003102  \n",
       "3       0.700925               0.763049           0.486810  \n",
       "4       0.266219               0.284211           0.245121  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vax_dates_wide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9a880cd0-b69e-49e5-940f-9ef5c099fb50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2400x2400 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot and stats\n",
    "\n",
    "fig = plt.figure(figsize=(4*4,4*4))\n",
    "axs = [fig.add_subplot(4,4,i+1) for i in range(4*4)]\n",
    "for di, dose in enumerate(['Dose1','Dose2','Dose3','TiterY1']):\n",
    "    for oi, outcome in enumerate(['VR_group','median_mmNorm','median_mmNorm_DTAPHib','median_mmNorm_PCV']):\n",
    "        ax = axs[(oi*4)+di]\n",
    "        # ax.text(0.5,0.5,dose+'\\n'+outcome)\n",
    "        if outcome == 'VR_group':\n",
    "            # categorical analysis -- boxplots\n",
    "            lvr_dat = vax_dates_wide[vax_dates_wide[outcome]=='LVR'][dose]\n",
    "            nvr_dat = vax_dates_wide[vax_dates_wide[outcome]=='NVR'][dose]\n",
    "            # stats\n",
    "            mwu = stats.mannwhitneyu(lvr_dat, nvr_dat)\n",
    "            # plot -- this is _nasty_ looking, work on that\n",
    "            bp = ax.boxplot([lvr_dat,nvr_dat], vert=False,\n",
    "                       patch_artist=True,\n",
    "                       labels=['LVR','NVR'])\n",
    "            VRcolours = ['tab:purple','tab:green']\n",
    "            for p,c in zip(bp['boxes'], VRcolours):\n",
    "                p.set_facecolor(c)\n",
    "            ax.text(0.95, 0.95, \n",
    "                    'p = '+str(round(mwu.pvalue,4)), # TODO: make this p value formatting better\n",
    "                    ha='right', va='top',\n",
    "                    transform=ax.transAxes)\n",
    "            \n",
    "        else:\n",
    "            # continous analysis -- scatter plots\n",
    "            xtp = vax_dates_wide[vax_dates_wide[outcome].notnull()][dose]\n",
    "            ytp = vax_dates_wide[vax_dates_wide[outcome].notnull()][outcome]\n",
    "            spearman = stats.spearmanr(xtp, ytp)\n",
    "            ax.scatter(xtp, ytp, color='k')\n",
    "            ax.text(0.95, 0.95, \n",
    "                    'p = '+str(round(spearman[1],4)), # TODO: make this p value formatting better\n",
    "                    ha='right', va='top',\n",
    "                    transform=ax.transAxes)\n",
    "            ax.set_xlabel('Age (days) at '+dose)\n",
    "            ax.set_ylabel(outcome)\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(plot_path, 'SuppFig_AgeAtVax.pdf'), dpi=600)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afdad8c9-66ea-4116-95ad-5db227d7a1d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c84516bc-7e88-4ae5-ab1e-683dc86923de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
